Study on the influencing factors of fish spatial distribution using three Bayesian models: a case study of Amblychaeturichthys hexanema in Haizhou Bay
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摘要: 栖息环境是生物生存的必要条件，生物与非生物因子共同影响海洋生物的空间分布。本研究以海州湾的六丝钝尾虾虎鱼（Amblychaeturichthys hexanema）为例，利用3种贝叶斯模型对2013−2022年春、秋季在海州湾进行的渔业资源底拖网调查和环境监测数据进行分析，探究六丝钝尾虾虎鱼的栖息分布特征以及主要影响因子。通过比较发现，贝叶斯正则化神经网络（BRNN）模型具有较好的拟合效果和预测性能，故本研究应用该模型进行分析。研究结果显示，六丝钝尾虾虎鱼的相对资源密度与饵料生物相对资源密度呈正相关关系；随着底层水温、底层盐度、水深、捕食者和竞争者的增加，六丝钝尾虾虎鱼的相对资源密度呈现先上升或保持相对平稳，而后下降的趋势。海州湾春、秋季六丝钝尾虾虎鱼的相对资源密度均呈现自西南向东北递减的趋势，且西南近岸浅海区的资源密度较高。秋季的资源密度高于春季，同时2018年、2021年和2022年秋季六丝钝尾虾虎鱼在34.7°～36°N、121°～121.6°E之间离岸较远的海域出现了资源聚集区。本研究将有助于深入了解六丝钝尾虾虎鱼的栖息分布特征及主要影响因素，为其资源养护和科学管理提供理论依据。Abstract: Habitat environment provides necessary condition for the survival of marine organisms. Biological and abiotic component jointly affect the spatial distribution of marine organisms. This study used three Bayesian models to analyze the bottom trawl survey data and environmental monitoring data during spring and autumn of 2013−2022 in Haizhou Bay, in order to explore the habitat distribution characteristics of Amblychaetorichthys hexanema and main influencing factors. It was found that the Bayesian regularized neural network (BRNN) model had good performance. Therefore, this study applied this model for analysis. The research results showed that there was a positive correlation between the relative resource density of A. hexanema and the relative density of its prey organisms. With the increase of bottom temperature, bottom salinity, water depth, predators and competitors, the relative density of A. hexanema rose firstly, and then declined. The relative density of A. hexanema in Haizhou Bay in spring and autumn showed a decreasing trend from southwest to northeast, and the density in the southwest coastal shallow area was relatively high. Among them, the density in autumn was higher than that in spring. At the same time, the A. hexanema in autumn of 2018, 2021, and 2022 showed higher resource density in offshore waters between 34.7°−36°N and 121°−121.6°E. This study will contribute to the understanding of habitat distribution characteristics of A. hexanema and main influencing factors, providing a theoretical basis for the conservation and management of its resource.
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图 2 海州湾春季和秋季六丝钝尾虾虎鱼空间分布影响因子的重要性
Fig. 2 Importance of influencing factors on spatial distribution of Amblychaeturichthys hexanemasix in the Haizhou Bay during spring and autumn
图 3 海州湾春季各因子与六丝钝尾虾虎鱼相对资源密度之间的关系（丰度单位：g/h）
Fig. 3 The relationship between various factors and the relative resource density of Amblychaeturichthys hexanemasix in the Haizhou Bay during spring (abundance unit: g/h)
图 4 海州湾秋季各因子与六丝钝尾虾虎鱼相对资源密度之间的关系（丰度单位：g/h）
Fig. 4 The relationship between various factors and the relative resource density of Amblychaeturichthys hexanemasix in the Haizhou Bay during autumn (abundance unit: g/h)
图 5 2013−2022年海州湾春季基于贝叶斯正则化神经网络模型的六丝钝尾虾虎鱼相对资源密度预测值与观测值的叠加图
Fig. 5 Overlapping maps of prediction and observations of relative density of Amblychaeturichthys hexanemasix in the Haizhou Bay based on Bayesian regularization neural network model in the spring from 2013 to 2022
图 6 2013−2022年海州湾秋季基于贝叶斯正则化神经网络模型的六丝钝尾虾虎鱼相对资源密度预测值与观测值的叠加图
Fig. 6 Overlapping maps of prediction and observations of relative density of Amblychaeturichthys hexanemasix in the Haizhou Bay based on Bayesian regularization neural network model in the autumn from 2013 to 2022
表 1 海州湾春季和秋季生物与非生物因子的多重VIF共线性检验
Tab. 1 VIF multicollinearity test of biotic and abiotic factors in the Haizhou Bay during spring and autumn
底层水温 底层盐度 水深 饵料生物 捕食者 竞争者 春季 2.34 1.93 4.24 1.37 1.21 1.64 秋季 1.74 3.61 2.13 1.61 1.13 1.64 
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表 2 海州湾春季和秋季3种贝叶斯模型拟合效果的比较
Tab. 2 Comparison of three Bayesian models in the Haizhou Bay during spring and autumn
季节 验证方法 统计参数 贝叶斯正则化神经网络模型 贝叶斯广义线性模型 贝叶斯岭回归模型 春季 模型拟合 R2 0.41 0.35 0.35 RMSE 1.60 1.68 1.69 交叉验证 R2 0.40 ± 0.08 0.37 ± 0.11 0.38 ± 0.11 RMSE 1.65 ± 0.12 1.69 ± 0.12 1.67 ± 0.12 秋季 模型拟合 R2 0.43 0.32 0.32 RMSE 1.76 1.91 1.92 交叉验证 R2 0.45 ± 0.08 0.41 ± 0.10 0.41 ± 0.11 RMSE 1.67 ± 0.12 1.72 ± 0.15 1.72 ± 0.16
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